class_docstr: Base class for acquisition functions in Bayesian Optimization
  
  :param model: GPyOpt class of model
  :param space: GPyOpt class of domain
  :param optimizer: optimizer of the acquisition. Should be a GPyOpt optimizer
docstr: Takes an acquisition and weights it so the domain and cost are taken into account.
docstr_tok: ["Takes","an","acquisition","and","weights","it","so","the","domain","and","cost","are","taken","into","account","."]
code: def acquisition_function(self, x):
      """
      Takes an acquisition and weights it so the domain and cost are taken into account.
      """
      f_acqu = self._compute_acq(x)
      cost_x, _ = self.cost_withGradients(x)
      x_z = (
          x
          if self.space.model_dimensionality
          == self.space.objective_dimensionality
          else self.space.zip_inputs(x)
      )
      return -(f_acqu * self.space.indicator_constraints(x_z)) / cost_x
  
file_path: GPyOpt/GPyOpt/acquisitions/base.py
imports: from ..core.task.cost import constant_cost_withGradients
label_desc: Use image node_id 3 for calling the AcquisitionBase obj's underlying member method code with example usage: obj.acquisition_function(x) and returns: unknown